A human-specific modifier of cortical connectivity and circuit function.

The cognitive abilities that characterize humans are thought to emerge from unique features of the cortical circuit architecture of the human brain, which include increased cortico-cortical connectivity. However, the evolutionary origin of these changes in connectivity and how they affected cortical circuit function and behaviour are currently unknown. The human-specific gene duplication SRGAP2C emerged in the ancestral genome of the Homo lineage before the major phase of increase in brain size1,2. SRGAP2C expression in mice increases the density of excitatory and inhibitory synapses received by layer 2/3 pyramidal neurons (PNs)3-5. Here we show that the increased number of excitatory synapses received by layer 2/3 PNs induced by SRGAP2C expression originates from a specific increase in local and long-range cortico-cortical connections. Mice humanized for SRGAP2C expression in all cortical PNs displayed a shift in the fraction of layer 2/3 PNs activated by sensory stimulation and an enhanced ability to learn a cortex-dependent sensory-discrimination task. Computational modelling revealed that the increased layer 4 to layer 2/3 connectivity induced by SRGAP2C expression explains some of the key changes in sensory coding properties. These results suggest that the emergence of SRGAP2C at the birth of the Homo lineage contributed to the evolution of specific structural and functional features of cortical circuits in the human cortex.

Machine learning to investigate superficial white matter integrity in early multiple sclerosis.

BACKGROUND AND PURPOSE: This study aims todetermine the sensitivity of superficial white matter (SWM) integrity as a metric to distinguish early multiple sclerosis (MS) patients from healthy controls (HC). METHODS: Fractional anisotropy and mean diffusivity (MD) values from SWM bundles across the cortex and major deep white matter (DWM) tracts were extracted from 29 early MS patients and 31 age- and sex-matched HC. Thickness of 68 cortical regions and resting-state functional-connectivity (RSFC) among them were calculated. The distribution of structural and functional metrics between groups were compared using Wilcoxon rank-sum test. Utilizing a machine learning method (adaptive boosting), 6 models were built based on: 1-SWM, 2-DWM, 3-SWM and DWM, 4-cortical thickness, or 5-RSFC measures. In model 6, all features from previous models were incorporated. The models were trained with nested 5-folds cross-validation. Area under the receiver operating characteristic curve (AUCroc ) values were calculated to evaluate classification performance of each model. Permutation tests were used to compare the AUCroc values. RESULTS: Patients had higher MD in SWM bundles including insula, inferior frontal, orbitofrontal, superior and medial temporal, and pre- and post-central cortices (p < .05). No group differences were found for any other MRI metric. The model incorporating SWM and DWM features provided the best classification (AUCroc = 0.75). The SWM model provided higher AUCroc (0.74), compared to DWM (0.63), cortical thickness (0.67), RSFC (0.63), and all-features (0.68) models (p < .001 for all). CONCLUSION: Our results reveal a non-random pattern of SWM abnormalities at early stages of MS even before pronounced structural and functional alterations emerge.